Anaesthesia monitoring using fuzzy logic

Objective. Humans have a limited ability to accurately and continuously analyse large amount of data. In recent times, there has been a rapid growth in patient monitoring and medical data analysis using smart monitoring systems. Fuzzy logic-based expert systems, which can mimic human thought processes in complex circumstances, have indicated potential to improve clinicians' performance and accurately execute repetitive tasks to which humans are ill-suited. The main goal of this study is to develop a clinically useful diagnostic alarm system based on fuzzy logic for detecting critical events during anaesthesia administration. Method. The proposed diagnostic alarm system called fuzzy logic monitoring system (FLMS) is presented. New diagnostic rules and membership functions (MFs) are developed. In addition, fuzzy inference system (FIS), adaptive neuro fuzzy inference system (ANFIS), and clustering techniques are explored for developing the FLMS' diagnostic modules. The performance of FLMS which is based on fuzzy logic expert diagnostic systems is validated through a series of offline tests. The training and testing data set are selected randomly from 30 sets of patients' data. Results. The accuracy of diagnoses generated by the FLMS was validated by comparing the diagnostic information with the one provided by an anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist's and FLMS's diagnoses. When detecting hypovolaemia, a substantial level of agreement was observed between FLMS and the human expert (the anaesthetist) during surgical procedures. Conclusion. The diagnostic alarm system FLMS demonstrated that evidence-based expert diagnostic systems can diagnose hypovolaemia, with a substantial degree of accuracy, in anaesthetized patients and could be useful in delivering decision support to anaesthetists.

Short term load forecasting using interval type-2 fuzzy logic systems

Accurate Short Term Load Forecasting (STLF) is essential for a variety of decision making processes. However, forecasting accuracy may drop due to presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. This paper proposes the application of Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) for the problem of STLF. IT2 FLSs, with extra degrees of freedom, are an excellent tool for handling prevailing uncertainties and improving the prediction accuracy. Experiments conducted with real datasets show that IT2 FLS models appropriately approximate future load demands with an acceptable accuracy. Furthermore, they demonstrate an encouraging degree of accuracy superior to feedforward neural networks used in this study.

Urbanheart surgery - a logic of design alternatives

In 1972 Sir Leslie Martin in his essay “The Grid as Generator”, advocated “a strong theoretical basis for [planning and] urban design” (Carolin P, 2000, p4) by methodically shifting design parameters regarding the way “in which buildings [could be] placed on the land” Martin was able to demonstrate how the generation of alternatives could “allow wider scope for decisions and objectives” to be considered and discussed (Carmona M, & Tiesdell S 2007, p81). Operating within a conventional design studio yet drawing of Sir Leslie Martin’s logic, ie developing an informed understanding of a problem by identifying a finite world of design ‘alternatives’, the following paper outlines a studio based program at the School of Architecture and Building, Deakin University, referred to as the ‘UrbanHeart Surgery’. While most atelier-based courses operate largely on an ad-hoc basis where students often work within self imposed competitive isolation, Urbanheart adopts a more open yet structured approach where students work in design collaboratives to generate a matrix of alternative design scenarios. The program actively integrates postgraduate students from Architecture, Urban Design and Planning into a design research culture and allows them to engage in critical discourse by working on strategic design projects in three areas significant to the future development of the state of Victoria: Metropolitan Urbanism, Urbanism on the Periphery and Regional Urbanism.

Interval type-2 fuzzy logic systems for load forecasting : a comparative study

Accurate short term load forecasting (STLF) is essential for a variety of decision-making processes. However, forecasting accuracy can drop due to the presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. This paper proposes the application of Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) for the problem of STLF. IT2 FLSs, with additional degrees of freedom, are an excellent tool for handling uncertainties and improving the prediction accuracy. Experiments conducted with real datasets show that IT2 FLS models precisely approximate future load demands with an acceptable accuracy. Furthermore, they demonstrate an encouraging degree of accuracy superior to feedforward neural networks and traditional type-1 Takagi-Sugeno-Kang (TSK) FLSs.

Prediction interval construction using interval type-2 fuzzy logic systems

This study proposes a novel non-parametric method for construction of prediction intervals (PIs) using interval type-2 Takagi-Sugeno-Kang fuzzy logic systems (IT2 TSK FLSs). The key idea in the proposed method is to treat the left and right end points of the type-reduced set as the lower and upper bounds of a PI. This allows us to construct PIs without making any special assumption about the data distribution. A new training algorithm is developed to satisfy conditions imposed by the associated confidence level on PIs. Proper adjustment of premise and consequent parameters of IT2 TSK FLSs is performed through the minimization of a PI-based objective function, rather than traditional error-based cost functions. This new cost function covers both validity and informativeness aspects of PIs. A metaheuristic method is applied for minimization of the non-linear non-differentiable cost function. Quantitative measures are applied for assessing the quality of PIs constructed using IT2 TSK FLSs. The demonstrated results for four benchmark case studies with homogenous and heterogeneous noise clearly show the proposed method is capable of generating high quality PIs useful for decision-making.

Fuzzy logic optimized wireless sensor network routing protocol

Wireless sensor networks (WSNs) are used in health monitoring, tracking and security applications. Such networks transfer data from specific areas to a nominated destination. In the network, each sensor node acts as a routing element for other sensor nodes during the transmission of data. This can increase energy consumption of the sensor node. In this paper, we propose a routing protocol for improving network lifetime and performance. The proposed protocol uses type-2 fuzzy logic to minimize the effects of uncertainty produced by the environmental noise. Simulation results show that the proposed protocol performs better than a recently developed routing protocol in terms of extending network lifetime and saving energy and also reducing data packet lost.

A new neural network-based type reduction algorithm for interval type-2 fuzzy logic systems

This paper introduces a new type reduction (TR) algorithm for interval type-2 fuzzy logic systems (IT2 FLSs). Flexibility and adaptiveness are the key features of the proposed non-parametric algorithm. Lower and upper firing strengths of rules as well as their consequent coefficients are fed into a neural network (NN). NN output is a crisp value that corresponds to the defuzzified output of IT2 FLSs. The NN type reducer is trained through minimization of an error-based cost function with the purpose of improving modelling and forecasting performance of IT2 FLS models. Simulation results indicate that application of the proposed TR algorithm greatly enhances modelling and forecasting performance of IT2 FLS models. This benefit is achieved in no cost, as the computational requirement of the proposed algorithm is less than or at most equivalent to traditional TR algorithms.

Review short: Rachael Briggs’s Free Logic

 Review of Rachael Briggs' poetry collection, Free Logic, UQP, 2013.

The Aristotelian spoudaios as ethical exemplar in Finnis’s natural law theory

One provocative but frequently overlooked feature of John Finnis’s natural law theory is its appeal to the normative role of the Aristotelian spoudaios (the mature person of practical reasonableness). Finnis’s account of the basic requirements of practical reasonableness and defense of the methodological device of “focal meaning” both have recourse to Aristotle’s claim that, in ethics and politics, things should be judged in terms of how they appear to the mature practically reasonable person. The current paper examines the normative role played by the spoudaios within Finnis’s natural law theory and provides a defense of that role against the objection that it lacks justificatory force because it is dependent upon circular reasoning. Section one contextualizes Finnis’s use of the spoudaios by considering its Aristotelian origins and also sketches some reasons for its demise in subsequent moral theory. This serves as the basis for an assessment in section two of whether Finnis’s employment of the spoudaios as an ethical exemplar conflates explanation and justification, and therefore culminates in decisionism. The conclusion of the paper is that Finnis’s recourse to the spoudaios is not viciously circular, because it is grounded in the reflexive and dialogical mode of justification proper to ethical enquiry.